Test of a Behavior-Based Individual-Based Model: Response of Shorebird Mortality to Habitat Loss

Abstract

In behavior-based individual-based models (IBMs), demographic functions are emergent properties of the model and are not built into the model structure itself, as is the case with the more widely used demography-based IBMs. Our behavior-based IBM represents the physiology and behavioral decision making of individual animals and, from that, predicts how many survive the winter nonbreeding season, an important component of fitness. This paper provides the first test of such a model by predicting the change in winter mortality of a charadriid shorebird following removal of intertidal feeding habitat, the main effect of which was to increase bird density. After adjusting one calibration parameter to the level required to replicate the observed mortality rate before habitat loss, the model predicted that mortality would increase by 3.65%, which compares well with the observed increase of 3.17%. The implication that mortality was density-dependent was confirmed by predicting mortality over a range of bird densities. Further simulations showed that the density dependence was due to an increase in both interference and depletion competition as bird density increased. Other simulations suggested that an additional area of mudflat, equivalent to only 10% of the area that had been lost, would be needed by way of mitigation to return mortality to its original level. Being situated at a high shore level with the flow of water in and out impeded by inlet pipes, the mitigating mudflat would be accessible to birds when all mudflats in the estuary were covered at high tide, thus providing the birds with extra feeding time and not just a small replacement mudflat. Apart from providing the first, and confidence-raising, test of a behavior-based IBM, the results suggest (1) that the chosen calibration procedure was effective; (2) that where no new fieldwork is required, and despite being parameter rich, a behavior-based IBM can be parameterized quickly (few weeks), and thus cheaply, because so many of the parameter values can be obtained from the literature and are embedded in the model; and (3) that behavior-based IBMs can be used to explore system behavior (e.g., the role of depletion competition and interference competition in density-dependent mortality).

A reanalysis of apparent survival rates of Indiana myotis (Myotis sodalis)

“…Individual-based models offer a method to estimate population-wide demographic parameters by determining the fate of individuals (Goss-Custard et al 2006a). Physiological and behavioral individual-based models have been successfully used to estimate survival in a variety of taxa (e.g., GossCustard et al 2006b;Railsback et al 2002) and provide at least 2 advantages when estimating survival rates.…”

“…First, demographic estimates are an emergent property of the model instead of an input variable. Therefore, resulting survival estimates are not directly linked to the set of environmental conditions under which the original demographic data were collected (Goss-Custard et al 2006a). Second, simulation models allow for modeling of survival under a range of conditions.…”

The Potential for the Use of Agent-Based Models in Ecotoxicology

“…The model was parameterized for the preimpact situation, and then run for the situation after the land reclamation and the increase in winter mortality were determined. The match of observed and predicted increase in winter mortality was strikingly good [66].…”

Ecology and conservation of Australia’s shorebirds

“…There is support for this kind of population limitation in non-breeding shorebirds (Goss-Custard et al, 1995;Burton et al, 2006;Goss-Custard et al, 2006). However, it is likely that factors not addressed in this thesis are also limiting shorebird populations (Colwell, 2010), and several of these factors could be interacting with anthropogenic habitat loss to accelerate population declines.…”

“…Additionally, one of the most widespread threats to shorebirds relates to the high levels of human disturbance (Paton et al, 2000;Milton & Harding, 2012;Weston et al, 2012). This leads to higher rates of energy expenditure which can reduce pre-migration departure condition and subsequent survival during migration (Pfister et al, 1992;West et al, 2002;Burger et al, 2004;Goss-Custard et al, 2006;Peters & Otis, 2007). A full understanding of the degree to which this variety of threats may be limiting populations is currently lacking.…”